1. Field of the Invention
The present invention relates generally to financial data processing, and more particularly, to a filtering method that is applied to financial data represented as a discrete-valued time series.
2. Description of the Related Art
With the use of electronic trading platforms by stock exchanges, the likelihood of ‘bad’ stock trades being reported (i.e. stock trades that are reported but did not actually take place) has increased. The bad stock trades may be the result of data entry errors or computer software bugs.
Eventually, the bad stock trades are detected and removed from the stock trade data that are reported, but this is typically not done in real-time or near real-time. There have been attempts to identify bad trades in real-time or near real-time by examining whether the percentage difference between two consecutive trading prices exceeds a fixed predefined percentage and filtering the latter trade as a bad trade if it does. Other methods identify any trade that is outside the range of the then-current bid and ask prices as a bad trade. These methods, however, are overly aggressive and tend to filter a large number of good trades along with the bad trades.